Analyses of polynomial neural networks for prediction of the prevalence of monkeypox infections in Asia and around the world
Priyanka Majumder 1 *
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1 Department of Basic Science and Humanities (Mathematics), Techno College of Engineering Agartala, Maheshkhola-799004, Tripura, INDIA* Corresponding Author


Monkeypox is a zoonosis disease that can spread from animals to people. Squirrels, rats taken from Gambian slums, dormice, various monkey species, and other animals have all shown signs of monkeypox virus infection. Contact with bodily fluids, sores on the skin or on internal mucosal surfaces, like those in the mouth or throat, respiratory droplets, and infected objects can all result in the spread of the disease. As the World Health Organization has warned the entire world against this disease, it is necessary to predict its prevalence in the entire world. This study uses a polynomial neural network model to predict monkeypox prevalence. Data on confirmed monkeypox cases collected from 6 May 2022 to 28 July 2022 are presented here. Based on the data, the prediction will be done using the group method of data handling model. The intensity of the spreading of this disease in the 100 days to come will be predicted in this study. The prediction will be done around the world, especially around the countries of the Asian continent which have been tremendously affected by the said disease.


This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Original Article

ELECTRON J GEN MED, 2022, Volume 19, Issue 6, Article No: em410

Publication date: 26 Aug 2022

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